基于Lasso-Logistic回归构建美沙酮维持治疗门诊患者继续使用毒品风险预测模型

刘雪娇; 罗巍; 张波; 闻品渊; 吴尊友

doi:10.16462/j.cnki.zhjbkz.2021.12.002

基于Lasso-Logistic回归构建美沙酮维持治疗门诊患者继续使用毒品风险预测模型

doi: 10.16462/j.cnki.zhjbkz.2021.12.002

1.
102206 北京，中国疾病预防控制中心性病艾滋病预防控制中心宣传教育与预防干预室
2.
650228 昆明，云南省药物依赖防治研究所

基金项目:

国家科技重大专项 2018ZX10721102

详细信息

通讯作者:
吴尊友，E-mail: wuzy@263.net

中图分类号: R749.053;R181.2
计量
- 文章访问数: 374
- HTML全文浏览量: 155
- PDF下载量: 78
- 被引次数: 0
出版历程
- 收稿日期: 2020-12-16
- 修回日期: 2021-01-26
- 网络出版日期: 2021-12-27
- 刊出日期: 2021-12-10

Based on Lasso-Logistic regression to build a continued drug use risk prediction model for methadone maintenance treatment patients

1.
Department of HIV Prevention and Intervention, National Center for AIDS/STD Control and Prevention, Chinese Center for Diseases Control and Prevention, Beijing 102206, China
2.
Yunnan Institute of Drug Abuse, Kunming 650228, China

Funds:

National Science and Technology Major Project 2018ZX10721102

More Information

Corresponding author: WU Zun-you, E-mail: wuzy@263.net

摘要

摘要: 目的探讨美沙酮维持治疗(methadone maintenance treatment, MMT)门诊患者入组治疗后继续使用毒品的影响因素，构建并验证继续使用毒品风险预测模型。方法以2013―2017年入组云南省MMT门诊的患者为研究对象，利用Lasso回归筛选出与患者继续使用毒品相关的变量，用于构建多因素Logistic回归分析模型；采用Bootstrap法进行模型内部验证并用列线图实现模型的可视化。结果本研究纳入的7 899名研究对象在接受治疗的6个月内，共有4 125(52.22%)人发生了继续使用毒品行为。Lasso回归筛选出9个与继续使用毒品相关的变量，分别是男性、独居、目前无工作、家庭关系一般、家庭关系较差、吸毒时长、过去1个月有注射行为、过去3个月曾被公安抓捕和过去3个月因吸毒而违法犯罪。预测模型的曲线下面积(area under curve, AUC)为0.70(95% CI: 0.69~0.72)。结论本研究构建的风险预测模型具有较好的预测能力，可用于指导MMT门诊工作人员提早识别出治疗期间继续使用毒品的高风险人群。
- 美沙酮维持治疗 /
- Lasso回归 /
- 风险预测模型 /
- 列线图
Abstract: Objective To explore the associated risk factors of continued drug use in methadone maintenance treatment (MMT) patients, and to build a continued drug use risk prediction model. Methods Opioid users who newly enrolled in MMT clinics in Yunnan Province during 2013-2017 were included in the study. We used Lasso regression analysis to screen risk factors for continued drug use. We constructed a prediction model based on screened risk factors above by multivariable Logistic regression analysis. We used bootstrap method to internally validate the model. Further, a nomogram was established to visualize the model. Results Of 7 899 eligible patients, 4 125(52.22%) cases continued to drug use in the first 6 months following enrollment. Nine variables collected at baseline for MMT patients were selected for the prediction model. i.e., male, living alone, unemployed, normal family relationship, bad family relationship, duration of drug use, drug injection behavior in past month, caught by the police in past three months, and criminal behavior in past three months. The model exhibited well discrimination with an area under curve (AUC) of 0.70 (95% CI: 0.69-0.72). Conclusion The model presents good performance in predicting continued drug use risk for MMT newly enrolled patients, which can be used to help clinic staff to find early patients at high-risk of continuing use opioid while receiving MMT service.
- Methadone maintenance treatment /
- Lasso /
- Risk prediction model /
- Nomogram

HTML全文

图 1 基于Lasso回归的特征变量选择(A: 十倍交叉验证图；B：收缩系数图)

Figure 1. Predictors selection based on Lasso regression (A: Ten fold cross-validation profile and B: LASSO coefficient profile)

下载: 全尺寸图片幻灯片

图 2 风险预测模型ROC曲线图

Figure 2. ROC curves of risk prediction model

下载: 全尺寸图片幻灯片

图 3 美沙酮门诊患者继续使用毒品风险预测列线图

Figure 3. Nomogram of risk prediction model of continued drug use in MMT patients

下载: 全尺寸图片幻灯片

表 1 MMT患者基线特征

Table 1. Baseline characteristics of MMT patients

基线特征	人数(例)	构成比(%)
性别
男	7 031	89.01
女	868	10.99
年龄(岁)
＜35	1 912	24.21
35~＜50	4 498	56.94
≥50	1 489	18.85
民族
汉族	5 155	65.26
非汉族	2 744	34.74
文化程度
文盲	566	7.17
小学/初中	6 415	81.21
高中及以上	918	11.62
婚姻状况
已婚有配偶	3 990	50.51
丧偶/离婚	1 244	15.75
未婚	2 665	33.74
居住状况
与家人/亲戚/朋友	6 783	85.87
独居	1 116	14.13
工作状况
有工作	2 533	32.07
无工作	5 366	67.93
家庭关系
良好	3 625	45.89
一般	2 049	25.94
较差	2 225	28.17
吸毒时长(年)
＜5	2 630	23.32
5~＜10	1 471	28.60
10~＜15	1 355	17.15
≥15	2 443	30.93
过去1个月内和吸毒朋友交往次数
< 1次/周	405	5.13
1~ < 7次/周	5 279	66.83
≥7次/周	2 215	28.04
入组MMT年份(年)
2013	2 421	30.65
2014	1 777	22.50
2015	1 565	19.81
2016	1 235	15.63
2017	901	11.41

下载: 导出CSV

表 2 多因素Logistic回归分析模型结果

Table 2. Results of multivariable Logistic regression model

变量	β值	Wald值	OR(95% CI)值	P值
性别
女性			1.00
男性	0.31	15.77	1.37(1.17~1.60)	< 0.001
居住状况
非独居			1.00
独居	0.64	69.27	1.90(1.63~2.20)	< 0.001
工作状况
有工作			1.00
无工作	0.49	85.80	1.63(1.47~1.80)	< 0.001
家庭关系
良好			1.00
一般	0.28	22.70	1.33(1.18~1.49)	< 0.001
较差	0.53	78.56	1.69(1.51~1.90)	< 0.001
吸毒时长(年)
< 5			1.00
5~ < 10	-0.14	3.89	0.87(0.76~1.00)	0.049
10~ < 15	-0.41	31.85	0.67(0.58~0.77)	< 0.001
≥15	-0.54	76.86	0.58(0.52~0.66)	< 0.001
过去1个月有注射行为
否			1.00
是	1.12	504.85	3.08(2.79~3.40)	< 0.001
过去3个月曾被公安抓捕
否			1.00
是	-0.48	25.92	0.62(0.52~0.75)	< 0.001
过去3个月因吸毒而违法犯罪
否			1.00
是	0.98	30.11	2.67(1.88~3.79)	< 0.001

下载: 导出CSV

参考文献(13)

[1]	Sullivan SG, Wu Z, Rou K, et al. Who uses methadone services in China? Monitoring the world's largest methadone programme[J]. Addiction, 2015, 110: 29-39. DOI: 10.1111/add.12781.
[2]	Moradinazar M, Farnia V, Alikhani M, et al. Factors related to relapse in patients with substance-related disorders under methadone maintenance therapy: decision tree analysis[J]. Oman Med J, 2020, 35(1): e89. DOI: 10.5001/omj.2020.07.
[3]	Yin W, Hao Y, Sun X, et al. Scaling up the national methadone maintenance treatment program in China: achievements and challenges[J]. Int J Epidemiol, 2010, 39: ii29-ii37. DOI: 10.1093/ije/dyq210.
[4]	Naji L, Dennis BB, Bawor M, et al. A prospective study to investigate predictors of relapse among patients with opioid use disorder treated with methadone[J]. Subst Abuse, 2016, 10: 9-18. DOI: 10.4137/sart.s37030.
[5]	Kassani A, Niazi M, Hassanzadeh J, et al. Survival analysis of drug abuse relapse in addiction treatment centers[J]. Int J High Risk Behav Addict, 2015, 4(3): e23402. DOI: 10.5812/ijhrba.23402.
[6]	Clark RE, Baxter JD, Aweh G, et al. Risk factors for relapse and higher costs among Medicaid members with opioid dependence or abuse: opioid agonists, comorbidities, and treatment history[J]. J Subst Abuse Treat, 2015, 57: 75-80. DOI: 10.1016/j.jsat.2015.05.001.
[7]	Sullivan SG, Wu Z, Cao X, et al. Continued drug use during methadone treatment in China: a retrospective analysis of 19, 026 service users[J]. J Subst Abuse Treat, 2014, 47(1): 86-92. DOI: 10.1016/j.jsat.2013.12.004.
[8]	Kelly JF, Hoeppner BB, Urbanoski KA, et al. Predicting relapse among young adults: psychometric validation of the Advanced WArning of RElapse (AWARE) scale[J]. Addict Behav, 2011, 36(10): 987-993. DOI: 10.1016/j.addbeh.2011.05.013.
[9]	Andersson HW, Wenaas M, Nordfj rn T. Relapse after inpatient substance use treatment: a prospective cohort study among users of illicit substances[J]. Addict Behav, 2019, 90: 222-228. DOI: 10.1016/j.addbeh.2018.11.008.
[10]	Flórez-Salamanca L, Secades-Villa R, Budney AJ, et al. Probability and predictors of cannabis use disorders relapse: results of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC)[J]. Drug Alcohol Depend, 2013, 132(1-2): 127-133. DOI: 10.1016/j.drugalcdep.2013.01.013.
[11]	Weinberger AH, Platt J, Esan H, et al. Cigarette smoking is associated with increased risk of substance use disorder relapse: a nationally representative, prospective longitudinal investigation[J]. J Clin Psychiatry, 2017, 78(2): e152-e160. DOI: 10.4088/JCP.15m10062.
[12]	Mohammadpoorasl A, Fakhari A, Akbari H, et al. Addiction relapse and its predictors: a prospective study[J]. J Addict Res Ther, 2012, 3(1): 1-3. DOI: 10.4172/2155-6105.1000122.
[13]	Shah NG, Galai N, Celentano DD, et al. Longitudinal predictors of injection cessation and subsequent relapse among a cohort of injection drug users in Baltimore, MD, 1988-2000[J]. Drug Alcohol Depend, 2006, 83(2): 147-156. DOI: 10.1016/j.drugalcdep.2005.11.007.